Wei Lan, Wu Yongsheng, Chen Lin, Cheng Jinquan, Zhao Jin
Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
J Med Internet Res. 2025 Mar 20;27:e69569. doi: 10.2196/69569.
The use of geosocial networking apps is linked to increased risky sexual behaviors among men who have sex with men, but their relationship with HIV and other sexually transmitted infections remains inconclusive. Since 2015, the prevalence of app use among men who have sex with men in Shenzhen has surged, highlighting the need for research on their spatiotemporal and behavioral patterns to inform targeted prevention and intervention strategies.
This study aims to investigate the population size, spatiotemporal and behavioral patterns, and mobility of app-using men who have sex with men in Shenzhen using mobile big data. The goal is to inform enhanced and innovative intervention strategies and guide health resource allocation.
By leveraging mobile big data application technology, we collected demographic and geographic location data from 3 target apps-Blued (Blued Inc), Jack'd (Online Buddies Inc), and Zank (Zank Group)-over continuous time periods. Spatial autocorrelation (Global Moran I) and hot spot analysis (Getis-Ord Gi) were used to identify the geographic clusters. The Geodetector tool (Chinese Academy of Sciences) was adopted to measure spatially stratified heterogeneity features.
From September 2017 to August 2018, a total of 158,387 males aged 15-69 years in Shenzhen used one of the 3 apps, with the majority (71,318, 45.03%) aged 25-34 years. The app user-to-male ratio was approximately 2.6% among all males aged 15-69 years. The estimated population of app-using men who have sex with men in Shenzhen during this period was 268,817. The geographic distribution of app-using men who have sex with men in Shenzhen was clustered, with hot spots primarily located in central and western Shenzhen, while the distribution of HIV testing and counseling was more concentrated in central-eastern Shenzhen. Approximately 60,202 (38%) app-using men who have sex with men left Shenzhen during the Spring Festival, and 37,756 (62.7%) of them returned after the holiday. The destination distribution showed a relatively centralized flow throughout the country, with the largest mobility within Guangdong province (67.7%), followed by lower mobility to Hunan province (7.9%) and other neighboring provinces (3%-5%), such as Jiangxi, Guangxi, and Hubei Provinces.
Shenzhen has a large population of men who have sex with men. The variation and inconsistent spatiotemporal distribution of app use and HIV testing and counseling emphasize the need to adapt traditional venue-based prevention and intervention to identified hot spots and to launch outreach initiatives that extend beyond traditional healthcare settings. Given the relatively high internal and interprovincial mobility of app-using men who have sex with men, further smartphone-based behavioral monitoring could provide valuable insights for developing enhanced and innovative HIV prevention and intervention strategies. Moreover, our study demonstrates the potential of mobile big data to address critical research gaps often overlooked by traditional methods.
使用地缘社交网络应用程序与男男性行为者中危险性行为的增加有关，但其与艾滋病毒及其他性传播感染的关系仍不明确。自2015年以来，深圳男男性行为者中应用程序的使用率激增，凸显了研究其时空和行为模式以制定针对性预防和干预策略的必要性。
本研究旨在利用移动大数据调查深圳使用应用程序的男男性行为者的人口规模、时空和行为模式以及流动性。目标是为强化和创新干预策略提供信息并指导卫生资源分配。
通过利用移动大数据应用技术，我们在连续时间段内从3个目标应用程序（Blued（Blued公司）、Jack'd（在线好友公司）和Zank（赞客集团））收集了人口统计学和地理位置数据。使用空间自相关（全局莫兰指数I）和热点分析（Getis-Ord Gi）来识别地理集群。采用地理探测器工具（中国科学院）来测量空间分层异质性特征。
2017年9月至2018年8月，深圳共有158,387名年龄在15 - 69岁的男性使用了这3个应用程序中的一个，其中大多数（71,318名，45.03%）年龄在25 - 34岁。在所有15 - 69岁男性中，应用程序用户与男性的比例约为2.6%。在此期间，深圳使用应用程序的男男性行为者估计人口为268,817人。深圳使用应用程序的男男性行为者的地理分布呈聚集状态，热点主要位于深圳中部和西部，而艾滋病毒检测和咨询的分布则更集中在深圳中东部。春节期间，约60,202名（38%）使用应用程序的男男性行为者离开深圳，其中37,756名（62.7%）在节后返回。目的地分布显示在全国范围内流动相对集中，广东省内流动性最大（67.7%），其次是前往湖南省的流动性较低（7.9%）以及其他邻近省份（3% - 5%），如江西、广西和湖北省。
深圳有大量男男性行为者。应用程序使用以及艾滋病毒检测和咨询的时空分布变化和不一致，强调了需要使传统的基于场所的预防和干预适应已确定的热点地区，并开展超越传统医疗环境的外展活动。鉴于使用应用程序的男男性行为者相对较高的省内和省际流动性，进一步基于智能手机的行为监测可为制定强化和创新的艾滋病毒预防和干预策略提供有价值的见解。此外，我们的研究证明了移动大数据在填补传统方法经常忽视的关键研究空白方面的潜力。
